Deep Learning-Assisted Analysis of Anomalous Nanoparticle Diffusion Near the Liquid Cell Surface Reveals the Effect of Electron Beam Dose Rate in TEM

The motion of nanoparticles near surfaces is of fundamental importance in physics, chemistry, and biology. Liquid cell transmission electron microscopy (LCTEM) is a promising technique for studying motion of nanoparticles with high spatial and temporal resolution. Yet, the lack of understanding of how the electron beam of a transmission electron microscope affects the particle motion has held back advancement in using LCTEM for in situ single nanoparticle and macromolecule tracking at interfaces. Here, we studied the diffusive motion of a model system of gold nanorods dispersed in water and moving near the silicon nitride membrane of a commercial liquid cell in a broad range of electron beam dose rates. Using a convolutional deep neural network model as well as canonical statistical tests, we showed that there is a crossover in diffusive behavior of nanoparticles in LCTEM from fractional Brownian motion at low dose rates, resembling diffusion in a viscoelastic medium, to continuous time random walk at high dose rates, resembling diffusion on an energy landscape with pinning sites. This understanding forms the foundation to use LCTEM for single nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.